This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantative finance, artificial intelligence and machine vision. What and how will I learn? The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of Machine Learning and Statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading edge research. Degree Structure Students undertake courses to the value of 180 credits. The programme consists of four core courses (60 credits), four optional courses (60 credits) and a research project (60 credits). Core Modules Supervised Learning Statistical Models and Data Analysis Applied Bayesian Methods EITHER: Graphical Models OR: Probabilistic and Unsupervised Learning Dissertationreport All MSc students undertake an independent research project, which culminates in a dissertation of 10,000 -12,000 words. Options Statistical Computing Mathematical Programming and Research Methods Machine Vision Decision and Risk Bioinformatics Information Retrieval Forecasting Stochastic Methods in Finance Advanced Topics in Machine Learning Evolutionary Systems Optimisation Approximate Inference and Learning in Probabilistic Models Further details available on subject website: http:www.csml.ucl.ac.ukcoursesmsc_csml The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process. Why should I study this degree at UCL? The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for Machine Learning and is scientific coordinator of the PASCAL2 European Network of Excellence. Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and the Department of Statistical Science, this MSc programme draws on world class research and teaching talents, and has excellent links with world-leading companies in internet technology, finance and related information areas. The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in Computational Statistics is also provided. Your future career There is a strong national and international demand for graduates with skills at the interface between traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities, and the UK has a number of very successful developers and suppliers of the technology. Areas in which expertise in statistics and machine learning is in particular demand include finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates also make excellent recruits for PhDs in statistics, machine learning or related areas. Entry Requirements A minimum of an upper second-class UK Bachelor's degree in computer science, statistics, mathematics, electrical engineering or the physical sciences or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. Students must be comfortable with undergraduate level mathematics. In particular it is normally expected that the candidate will have knowledge of statistics at an intermediate undergraduate level and will be proficient in linear algebra and multivariable calculus. How to apply Students are advised to apply as early as possible due to competition for pl
This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantative finance, artificial intelligence and machine vision. What and how will I learn? The programme aims to provide graduates with the foundational principles and the practical experience neede...
